Automatically detecting characteristics of a medical image series

ABSTRACT

Techniques are described for automatically detecting scan characteristics of a medical image series. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise an image generation component that generates a representative image of a medical image series comprising a plurality of scan images, and a series characterization component that processes the representative image using one or more characteristic detection algorithms to determine one or more characteristics of the medical image series. The system can further tailor the visualization layout for viewing the medical image series based on the one or more characteristics and/or automatically perform various workflow tasks based on the one or more characteristics.

TECHNICAL FIELD

This application relates to automatically detecting the characteristicsof a medical image series.

BACKGROUND

The healthcare industry has innumerable opportunities to leverageartificial intelligence (AI), machine learning (ML), and otheranalytical models to achieve more accurate, proactive, and comprehensivepatient care. From reducing administrative burdens to supportingprecision medicine, these analytical tools are showing promise acrossclinical, financial, and operational domains. Learning algorithms, forinstance, can become more precise and accurate as they interact withtraining data, allowing humans to gain unprecedented insights intodiagnostics, care processes, treatment variability, and patientoutcomes. However, even organizations with industry-leading analyticscompetencies in hand are facing complex challenges when it comes toapplying various analytics to clinical care.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the invention. This summary is not intended toidentify key or critical elements or delineate any scope of thedifferent embodiments or any scope of the claims. Its sole purpose is topresent concepts in a simplified form as a prelude to the more detaileddescription that is presented later. In one or more embodimentsdescribed herein, systems, computer-implemented methods, apparatusand/or computer program products are provided that facilitateautomatically detecting scan characteristics of a medical image series.

According to an embodiment, a system is provided that comprises a memorythat stores computer executable components, and a processor thatexecutes the computer executable components stored in the memory. Thecomputer executable components comprise an image generation componentthat generates a representative image of a medical image seriescomprising a plurality of scan images. In particular, the medical imageseries can include two-dimensional (2D) anatomy scans generated fromthree-dimensional (3D) medical imaging scan data representative of athree-dimensional (3D) volume of an anatomical region of a patient, suchas that generated using a computed tomography (CT) scan, a magneticresonance imaging (MRI) scan, or another 3D medical imaging scanningmodality. The image generation component generates the representativeimage from the 3D data using a novel to 2D image compression processes.

The computer executable components further comprise a seriescharacterization component that processes the representative image usingone or more characteristic detection algorithms to determine one or morecharacteristics of the medical image series. The more characteristicdetection algorithms can include but are not limited to, a scan typedetection algorithm, an anatomic region detection algorithm, a contrastphase detection algorithm, and a foreign object detection algorithm. Inthis regard, the one or more scan characteristics can include, but arenot limited to, the type of the scan, the anatomical region or regionsscanned, the iodinated contrast phase represented, and presence/absenceof foreign objects (e.g., metal implants/devices).

In various embodiments, the one or more characteristic detectionalgorithms comprise a series of detection algorithms applied to therepresentative image in series and the scan characterization componentselects subsequent detection algorithms in the series based on resultsof preceding detection algorithms in the series. For example, the one ormore characteristics detection algorithms can comprise a first detectionalgorithm and a second detection algorithm, and wherein the scancharacterization component selects the second detection algorithm fromamongst a plurality of second detection algorithms based on the resultsof the first detection algorithm. In one or more embodiments, the firstdetection algorithm comprises a scan type detection algorithm adapted todetect a type of the medical image series wherein the second detectionalgorithms are tailored to different types of medical image series.

The system can further tailor the visualization layout for viewing themedical image series based on the one or more characteristics and/orautomatically perform various workflow tasks based on the one or morecharacteristics. For example, based on the detected characteristics ofthe medical image series, the system can automatically call and applyappropriate medical image processing workflows to the medical imageseries, find and pull related cases for the same patient forlongitudinal study analysis, find and pull related cases for differentpatients for comparative analysis, and the like.

In some embodiments, elements described in the disclosed systems can beembodied in different forms such as a computer-implemented method, acomputer program product, or another form.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a high-level flow diagram of an example computerimplemented process for automatically detecting scan characteristics ofa medical image series for workflow automation in accordance with one ormore embodiments of the disclosed subject matter.

FIG. 2 illustrates a block diagram of an example, non-limiting computingsystem that facilitates automatically detecting scan characteristics ofa medical image series in accordance with one or more embodiments of thedisclosed subject matter.

FIG. 3 illustrates an example process for generating one or morerepresentative images for a medical image scan series in accordance withone or more embodiments of the disclosed subject matter.

FIGS. 4A-4C present example scan representative images generated fordifferent medical image series type classifications in accordance withone or more embodiments of the disclosed subject matter.

FIG. 5 present an example visualization layout for a brain scan seriesin accordance with one or more embodiments of the disclosed subjectmatter.

FIG. 6 present an example visualization layout for a trauma torso seriesin accordance with one or more embodiments of the disclosed subjectmatter.

FIG. 7 illustrates a flow diagram of an example process forautomatically detecting scan characteristics of a medical image seriesin accordance with one or more embodiments of the disclosed subjectmatter.

FIG. 8 illustrates a flow diagram of another example process forautomatically detecting scan characteristics of a medical image seriesin accordance with one or more embodiments of the disclosed subjectmatter.

FIG. 9 illustrates a high-level flow diagram of another example processfor automatically detecting scan characteristics of a medical imageseries in accordance with one or more embodiments of the disclosedsubject matter.

FIG. 10 illustrates a block diagram of an example, non-limitingoperating environment in which one or more embodiments described hereincan be facilitated.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background section,Summary section or in the Detailed Description section.

The disclosed subject matter is directed to systems,computer-implemented methods, apparatus and/or computer program productsthat facilitate automatically detecting scan characteristics of amedical image series for workflow automation. The disclosed techniquespropose an optimized single image representation of a medical imageseries (e.g., a CT image series). The representative images are used totrain one or more intelligent algorithm (e.g., deep learning networks)to automatically detect specific exam characteristics of that imageseries. The characteristics can include (but are not limited to): thetype of the series/exam; the anatomic region was scanned; the iodinatedcontrast phase present in the series images; and the presence or absenceof metal implants/devices.

The goal of the disclosed techniques is to simplify the subsequentworkflow steps (e.g., in trauma workflows and other) in an exam wheremultiple image series are present. Today it is a manual process fortechnologists and clinicians to determine what each image seriesrepresents and then set up the visualization layouts, post-processingsteps, and analysis tools for each series. This invention aims tosignificantly automate the process. In this regard, one of the drawbacksof medical imaging systems today is the image series description tagsare not consistently filled in accurately or compliantly in the field(e.g., in the Digital Imaging and Communications in Medicine (DICOM)format or another format). For example, series descriptions vary betweensites/regions, and are editable by the technicians at time of scan ifnecessary. In addition, scan ranges and injection protocols varydepending on exams and patients and these parameters are often notrecorded by the technician or the imaging system at the time of thescan. This is particularly the case in acute care settings such asemergency room (ER) settings and similar time/resource constrainedsettings. In such settings, even the scan protocols that are used may bethose that were set up for a different patient or purpose then neededfor the current patient and use-case. Thus, there is no guarantee thatwhat the series description and tags indicate provided accurately depictthe correct contents of the image series.

The disclosed systems and methods overcome these challenges by providinga technique wherein an entire image series can be represented as asingle representative image. One or more machine learning (e.g., deeplearning or the like) series characterization algorithms (or models) arethen used to classify the representative image into various categories.In addition, multiple models, each for a specific task ofcharacterization, can be applied in sequence to provide multiple levelsof information that can be used in the final use-case. For example, ananatomy detection model can be used to select which contrast phasedetection model (e.g., specific for an anatomic region) should be run.Similarly, this can next be used to select and run an artifact detectionmodel for that anatomy. The models can also be used in conjunction withDICOM tags for reconstruction parameters and relevant patientinformation (e.g., demographic information, medical history information,etc.) to facilitate automating the analysis and workflow even further.

For example, in various embodiments, based on the automatically detectedscan characteristics, the disclosed system can automatically optimizethe visualization layout presented to clinicians for reviewing the scanimages. This is a game changer in trauma and oncology scans that requirea significant number of series to review with variable protocols,significantly reducing the amount of manual intervention needed todayfrom technicians and clinicians. For example, in the trauma workflow,where the exams often contain multiple series of different anatomyregions in the body (sometimes up to a dozen), the visualization layoutsfor each series can be set up automatically with the correct displaysettings. In addition, the appropriate tools for an anatomy or type ofscan can be made available for only that window where that series isdisplayed.

The automatically detected scan characteristics can also be used toautomatically invoke the appropriate post-processing applications. Forexample, automated algorithms and analysis can be run for each serieswithout needing the clinician to manually invoke them. For instance, inone example use-case for an automated cardiac exam, the exam typedetection can determine which series is the most appropriate for calciumscoring analysis and which is for the coronary analysis. The analysissteps for each can be automatically invoked and reports generated.Similarly knowing the content of each series, even specific heartchamber segmentation algorithms or other image processing algorithms(e.g., organ segmentation, fat segmentation, lesion detection/scoring,and the like) can be invoked automatically and added to the report.

In various embodiments, the medical image series/exams include CT scans,including multi-energy CT images and material images for dual energy CTand spectral CT. However, the disclosed techniques can be applied toother 3D medical imaging modalities, including but not limited to, MRI,positron emission tomography (PET), ultrasound, and the like. Thedisclosed techniques are further anatomy and acquisition protocol (e.g.,contrast/non-contrast) agnostic.

In this regard, the types of medical images processed/analyzed using thetechniques described herein can include images captured using varioustypes of image capture modalities. For example, the medical images caninclude (but are not limited to): radiation therapy (RT) images, X-ray(XR) images, digital radiography (DX) X-ray images, X-ray angiography(XA) images, panoramic X-ray (PX) images, computerized tomography (CT)images, mammography (MG) images (including a tomosynthesis device), amagnetic resonance imaging (MR) images, ultrasound (US) images, colorflow doppler (CD) images, position emission tomography (PET) images,single-photon emissions computed tomography (SPECT) images, nuclearmedicine (NM) images, and the like. The medical images can also includesynthetic versions of native medical images such as synthetic X-ray(SXR) images, modified or enhanced versions of native medical images,augmented versions of native medical images, and the like generatedusing one or more image processing techniques.

A “capture modality” as used herein refers to the specific technicalmode in which an image or image data is captured using one or moremachines or devices. In this regard, as applied to medical imaging,different capture modalities can include but are not limited to: a 2Dcapture modality, a 3D capture modality, an RT capture modality, a XRcapture modality, a DX capture modality, a XA capture modality, a PXcapture modality a CT, a MG capture modality, a MR capture modality, aUS capture modality, a CD capture modality, a PET capture modality, aSPECT capture modality, a NM capture modality, and the like.

The term “multiphase” as used herein with respect to medical imagingrefers to capture of image data of the same patient/anatomy using a samecapture modality yet under different conditions. In various embodiments,the different conditions can include different acquisition protocols,different acquisition prescription planes (e.g., capture orientation),and/or different physiological phases. The resulting image data caninclude different sets or series of medical images captured inassociation with each of the different phases.

In various embodiments, the different physiological phases can be basedon contrast injection. For example, the dual vascular supply of liver(75% portal venous and 25% hepatic arterial) results in sequentialopacification of hepatic arteries, portal veins, and hepatic veins afterinjection of intravenous contrast. Different tissues and structuresreach peak enhancement at different times. This allows the acquisitionof images during different time ranges or “dynamic phases” to highlightthese differences. In this regard, multiphase MR and/or multiphase CTcan refer to image acquisition at sequential time ranges before andafter contrast administration. While these phases are a continuum, theyare described as distinct time ranges for simplicity and clinicalutility. In some embodiments, multiphase MR and/or multiphase CT caninclude image data captured two or more of the following phases:pre-contrast phase (or unenhanced phase), intravenous (IV) phase (IVP),arterial phase (AP), early AP, late AP, extracellular phase (ECP),portal venous phase (PVP), delayed phase (DP), transitional phase (TP),hepatobiliary phase (HBP), and variants thereof.

As used herein, a “3D image” refers to digital image data representingan object, space, scene, and the like in three dimensions, which may ormay not be displayed on an interface. 3D images described herein caninclude data representing positions, geometric shapes, curved surfaces,and the like. In an aspect, a computing device, such as a graphicprocessing unit (GPU) can generate a 3D image based on the data,performable/viewable content in three dimensions. For example, a 3Dimage can include a collection of points represented by 3D coordinates,such as points in a 3D Euclidean space (e.g., a point cloud). Thecollection of points can be associated with each other (e.g., connected)by geometric entities. For example, a mesh comprising a series oftriangles, lines, curved surfaces (e.g. non-uniform rational basissplines (“NURBS”)), quads, n-grams, or other geometric shapes canconnect the collection of points. In an aspect, portions of the mesh caninclude image data describing texture, color, intensity, and the like.

A 3D anatomy image refers to a 3D or volumetric representation of ananatomical region of a patient. In some implementations, a 3D anatomyimage can be captured in 3D directly by the acquisition device andprotocol. In other implementations, a 3D anatomy image can comprise agenerated image that was generated from one-dimensional (1D)two-dimensional (2D) and/or 3D sensory and/or image data captured of theanatomical region of the patient. Some example 3D medical images include3D volume images generated from CT scan data, and MRI scan data. It isnoted that the terms “3D image,” “3D volume image,” “volume image,” “3Dmodel,” “3D object,”, “3D reconstruction,” “3D representation,” “3Drendering,” and the like are employed interchangeably throughout, unlesscontext warrants particular distinctions among the terms. It should beappreciated that such terms can refer to data representing an object, ananatomical region of the body, a space, a scene, and the like in threedimensions, which may or may not be displayed on an interface. The terms“3D data,” and “3D image data” can refer to a 3D image itself, datautilized to generate a 3D image, data describing a 3D image, datadescribing perspectives or points of view of a 3D image, capture data(e.g., sensory data, images, etc.), meta-data associated with a 3Dimage, and the like. It is noted that the term “2D image” as used hereincan refer to data representing an object, an anatomical region of thebody, a space, a scene, and the like in two dimensions, which may or maynot be displayed on an interface.

The term “3D anatomy scan data” is used herein to refer to thecollection of scan data acquired/generated in association with aperformance of a 3D medical imaging scan, such as a CT scan, an MRIscan, a PET scan or the like. For example, 3D anatomy scan data caninclude 1D, 2D and 3D data that can be used to generate a 3D volumetricimage of the scanned anatomy and to generate 2D scan imagescorresponding to slices of the 3D volumetric image from variousperspective/orientations (e.g., relative to the axial plane, the coronalplane, the sagittal plane and other reformatted views). The terms “3Danatomy scan data,” “3D anatomy data,” “3D scan data,” and the like areused herein interchangeably unless context warrants particulardistinctions amongst the terms. The term “scan slice,” “image slice,”“scan image,” and the like are used herein interchangeably to refer to areconstructed 2D image generated from 3D anatomy scan data thatcorresponds to a computer-generated cross-sectional image of ananatomical region of a patient.

The terms “thick” and “thin” as applied to a scan image/slice are usedherein to refer to the relative thickness of the tissue represented inthe slice, which can vary depending on the scanner detector. It shouldbe appreciated that a thin slice has a smaller thickness than a thickslice. In accordance with most 3D medical imaging modalities (e.g., CT,MRI, PET, etc.), the native resolution of thin scan images (e.g.,obtained with thin detectors) is higher than the native resolution ofthicker scan images (e.g., obtained with thicker detectors). Forexample, the nominal slice thickness in CT is defined as the full widthat half maximum (FWHM) of the sensitivity profile, in the center of thescan field; its value can be selected by the operator according to theclinical requirement and generally lies in the range between 1millimeter (mm) and 10 mm In general, the larger the slice thickness,the greater the low contrast resolution in the image, while the smallerthe slice thickness, the greater the spatial resolution.

The term “multimodal data” is used herein to refer to two or moredifferent types of data. The differentiation factor between the two ormore different types of data can vary. For example, the differentiationfactor can refer to the medium of the data (e.g., image data, text data,signal data, etc.), the format of the data, the capture modality of thedata, the source of the data and so one. In the medical/clinicalcontext, multimodal clinical refers to two or more forms ofhealth-related information that is associated with patient care and/orpart of a clinical trial program. Clinical data consist of informationranging from determinants of health and measures of health and healthstatus to documentation of care delivery. Different types of clinicaldata are captured for a variety of purposes and stored in numerousdatabases across healthcare systems. Some example types of clinical datathat may be included in a pool of multimodal clinical data from which adata cohort may be generated includes (but is not limited to): medicalimages and associated metadata (e.g., acquisition parameters), radiologyreports, clinical laboratory data, patient electronic health record(EHR) data, patient physiological data, pharmacy information, pathologyreports, hospital admission data, discharge and transfer data, dischargesummaries, and progress notes.

The term “clinical inferencing model” is used herein to refer to a MLmodel configured to perform a clinical decision/processing on clinicaldata. The clinical decision/processing task can vary. For example, theclinical decision/processing tasks can include classification tasks(e.g., disease classification/diagnosis), diseaseprogression/quantification tasks, organ segmentation tasks, anomalydetection tasks, image reconstruction tasks, and so on. The clinicalinferencing models can employ various types of ML algorithms, including(but not limited to): deep learning models, neural network models, deepneural network models (DNNs), convolutional neural network models(CNNs), generative adversarial neural network models (GANs), longshort-term memory models (LSTMs), attention-based models, transformersand the like. The term “multimodal clinical inferencing model” is usedherein to refer to a clinical inferencing model adapted to receive andprocess multimodal clinical data as input. Various embodiments of thedisclosed subject matter are directed to an oxygen forecasting supportmodel that can be or include a multimodal clinical inferencing modeladapted to forecast short-term oxygen support needs for patients with arespiratory condition.

As used herein, a “medical imaging inferencing model” refers to an imageinferencing model that is tailored to perform an imageprocessing/analysis task on one or more medical images. For example, themedical imaging processing/analysis task can include (but is not limitedto): scan series characteristic classification, disease/conditionclassification, disease region segmentation, organ segmentation, diseasequantification, disease/condition staging, risk prediction, temporalanalysis, anomaly detection, anatomical feature characterization,medical image reconstruction, and the like. The terms “medical imageinferencing model,” “medical image processing model,” “medical imageanalysis model,” and the like are used herein interchangeably unlesscontext warrants particular distinction amongst the terms.

One or more embodiments are now described with reference to thedrawings, wherein like referenced numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea more thorough understanding of the one or more embodiments. It isevident, however, in various cases, that the one or more embodiments canbe practiced without these specific details.

Turning now to the drawings, FIG. 1 illustrates a high-level flowdiagram of an example computer implemented process 100 for automaticallydetecting scan characteristics of a medical image series in accordancewith one or more embodiments of the disclosed subject matter.

In accordance with process 100, at 104, one or more representativeimages 106 are generated for one or more scan series represented in scandata 102. In this regard, the scan data 102 can include medical imageseries data that includes a series of 2D scan images generated inassociation with performance of a 3D medical imaging scan, such as a CTscan, an MRI scan, a PET scan or the like. As applied to CT, the scanimages can be acquired either with single energy, dual energy, or photoncounting CT. For dual energy or photon counting CT, an iodine map can beused to characterize the contrast phase of the scan. The 2D scan imagesmay include native scan images that are generated relative to theirnative acquisition plane and/or reconstructed images that areregenerated from the 3D scan image volume data relative to a differentacquisition plane. In some implementations, the scan data 102 mayinclude two or more different medical image series. For example, the twoor more different medical image series can correspond to differentperspectives/orientations of the same anatomical region of interest(ROI) captured, different acquisition cycles, different contrast phasesof a perfusion scan, and/or different ROIs. With these implementations,one or more representative images can be generated for each (or in someimplementations one or more) scan series represented in the scan data102.

In some implementations, the scan data 102 can also include or otherwisebe associated with metadata describing characteristics of the medicalimage series associated therewith. For example, the metadata can includeidentifying or indicating the type of the scan, the ROI scanned, theacquisition protocols used, the scan mode used, the acquisition plane,and the scanner device/system used. The metadata can also includeinformation regarding the reconstruction parameters used to generate the2D scan images included in the series (e.g., reconstruction kernel size,slice thickness, the modulation transfer function (MTF) value, slicesensitivity profile (SSP), point spread function (PSF) characteristics,etc.). The metadata may also include information identifying orindicating the relative orientation of the medical image series (e.g.,axial, coronal, sagittal, or another orientation) and/or the relativelocation/position of each (or in some implementation one or more) scanimage included in the series in 3D. In some implementations in which thescan was performed with perfusion, the metadata may also include timestamp information that identifies or indicates the relative timing ofcapture of each scan relative to perfusion process and/or the relativephase of the perfusion process at which time each scan image wascaptured/generated. The metadata may also include patient information,such as information identifying or indicating (e.g., in an anonymizedmanner as appropriate) the identity of the patient (e.g., a uniquepatient identifier such a name or anonymized identification number),demographic information for the patient (e.g., age, gender, body massindex (BMI), and relevant medical history information for the patient(e.g., comorbidities, current condition/diagnosis, current pathology,reason for performance of the scan, etc.). In some embodiments, thismetadata and/or patient information can be used as input in paralleland/or in combination with the one or more representative images 106 at108 into one or more series characterization models to facilitateinferring the series characteristics, as discussed in greater detailbelow.

The scan data 102 can also include or otherwise be associated with 3Dvolume data that represents a 3D representation of the anatomical ROIcaptured during the 3D imaging scan. The 3D volume data can include orotherwise be associated with information that defines the relativegeometry of the 3D volume and the relative position/orientation of each2D scan image relative to the 3D volume. For example, the 3D volume datacan be associated with metadata that defines the 3D bounding box of theROI captured. In some implementations, the 3D volume data may also beassociated with metadata describing the relative geometry and positionof one or more anatomical features of interest reflected in the 3Dvolume data and/or the relative position of one or more scan planes.

The goal of the representative image generation process at 104 is togenerate a single (or in some implementations two or more representativeimages) representative image 106 for each scan series included in thescan data 102 that captures the entire contents of the characteristicsof the scan series. At a high level, the process of generating therepresentative image 106 can be correlated to a 3D to 2D imagecompression process that compresses the 3D volume image data representedby the stacked scan series images into a single 2D image that providesthe most optimal representation of the entire image series for thepurpose of automatically detecting defined characteristics of the scanseries that may or may not be included in metadata describing the scanseries. Techniques for generating the representative image 106 arediscussed in greater detail infra with reference to FIG. 2 and the imagegeneration component 210.

At 108, the representative image 106 is used to automatically infercharacteristics of the series that may or may not be included inmetadata describing the scan series. In this regard, as described above,many scan series may be received with no, minimal, or incorrect metadatadescribing relevant characteristics of the scan series, such as (but notlimited to), the scan type, the anatomical ROI scanned, the contrastphase captured, and presence/absence of metal implants/devices. Thedisclosed techniques train deep learning models to automatically detectspecific scan characteristics from the representative image(s) generatedat 104. These deep learning models are referred to herein as seriescharacterization models. In this regard, at 108, one or more seriescharacterization models can be applied to the representative image 106to generate scan series characteristics data 110 that provides one ormore defined characteristics of each series represented in the scan data102. The specific characteristics include in the scan series data 110can vary depending on the type of the scan series and the availableseries characterization models that are applicable to the scan series.In one or more embodiments, these characteristics may include (but arenot limited to), the scan type, the anatomical ROI scanned, the contrastphase depicted (if applicable), and information identifyingpresence/absence of metal implants/devices.

As described in greater detail infra, in various embodiments, the seriescharacterization models that are applied to the representative image 106can include a plurality of different models adapted to detect differentcharacteristics. More importantly, the models can be applied in adaisy-chain fashion, wherein at least some of the models applied can beselected based on the output of one or more preceding models. Forexample, in some embodiments, a first model can be applied that isadapted to detect the type of the scan series. Once the scan series typehas been determined, the next model that is applied can be selected fromamongst a pool of different models that is specifically tailored to thatscan type. The number of sequential models applied that are selectedbased on the output results of a preceding model can vary.

The result of the series characteristics inference process at 108provides scan series characteristics data 110 describing the inferred(or automatically detected) characteristics of each scan series includedin the scan data 102. At 112, the scan series characteristics data 110can be employed to perform a variety of different output utilizations.For example, the scan series characteristics data 110 can be provided asfeedback to the imaging system operating techniques for the purpose ofquality control to ensure the correct/desired scan seriescharacteristics were captured. This process can be performed during thescan and/or immediately upon completion of the scan while the patientremains on the scanner table so that the patient can be rescanned asneeded. The system can further tailor the visualization layout forviewing the medical image series based on the one or morecharacteristics and/or automatically perform various workflow tasksbased on the one or more characteristics. For example, based on thedetected characteristics of the medical image series, the system canautomatically call and apply appropriate medical image analysis toolsand processing workflows to the medical image series. For instance, theanalysis tools can include clinical inferencing models adapted toperform pathology detection and/or characterization, perform organsegmentation, perform image quality enhancement, and the like. Thesystem can also invoke appropriate applications for the medical imagesseries based on the detected series characteristics. The system can alsofind and pull related cases for the same patient for longitudinal studyanalysis, find and pull related cases for different patients forcomparative analysis. Various other output utilizations are envisioned.

FIG. 2 illustrates a block diagram of an example, non-limiting computingsystem 200 that facilitates automatically detecting scan characteristicsof a medical image series in accordance with one or more embodiments ofthe disclosed subject matter. Repetitive description of like elementsemployed in respective embodiments is omitted for sake of brevity.

Embodiments of systems described herein can include one or moremachine-executable components embodied within one or more machines(e.g., embodied in one or more computer-readable storage mediaassociated with one or more machines). Such components, when executed bythe one or more machines (e.g., processors, computers, computingdevices, virtual machines, etc.) can cause the one or more machines toperform the operations described.

In this regard, computing system 200 can be and/or include variouscomputer executable components. In the embodiment shown, these computerexecutable components include series characterization module 208,reception component 220, visualization component 222, feedback component224, similar case study component 226 and post-processing component 228.These computer/machine executable components (and other describedherein) can be stored in memory associated with the one or moremachines. The memory can further be operatively coupled to at least oneprocessor, such that the components can be executed by the at least oneprocessor to perform the operations described. Examples of said andmemory and processor as well as other suitable computer orcomputing-based elements, can be found with reference to FIG. 10 (e.g.,with reference to processing unit 1004 and system memory 1006), and canbe used in connection with implementing one or more of the systems orcomponents shown and described in connection with FIG. 1 or otherfigures disclosed herein.

System 200 can further include or be operatively coupled to an imagingsystem 202 and/or a picture archiving communication system (PACS) 204.The imaging system 202 can correspond to a 3D medical image system thatcaptures and provides the scan data 102. For example, the imaging system202 can correspond to a CT imaging system, an MR imaging system, or thelike. Although a single imaging system is depicted, it should beappreciated that the imaging system 202 can correspond to a plurality ofdisparate imaging systems. In some embodiments, the reception component220 can receive the scan data 102 directly from the imaging system 202.Additionally, or alternatively, scan data (e.g., scan data 102) providedby the imaging system 202 can be stored in one or more medical imagedatabases 206 provided by the PACS 204 and the reception component 220can extract the scan data 102 from the PACS 204.

System 200 can further include one or more user devices 230. The userdevice 230 can provide for accessing and employing the various outpututilizations of related to the scan series characterization data 106.For example, the user device 230 can be or include a display capable ofrendering medical image series included in the scan data 102 accordingto a visualization layout set up based on the scan seriescharacteristics data 106. The user device 230 can also include suitablehardware and software that provides for communicating with various othercomponents/system and modules of system 200 (e.g., via one or more wiredor wireless communication networks), executing applications, and thelike. In this regard, the type of the user device 230 can vary. Forexample, the user device 230 can be or include a server device, apersonal computing device, a laptop computer, a desktop computer, atablet computer, a smartphone, a television, a monitor, a wearabledevice, and the like. System 200 further includes a system bus 232 thatcan communicatively or operatively coupled the various systems, modules,components and devices of system 200. In some embodiments, the systembus 232 can include or correspond to one or more wired or wirelesscommunication networks.

The architecture of system 200 can vary. For example, one or morecomponents of the computing system 200 can be deployed on the samecomputing device (e.g., the scanner device/system used toacquire/capture and generate the input scan images). Additionally, oralternatively, one or more components of the computing system 200 can bedeployed at different communicatively coupled computing devices (e.g.,via one or more wired or wireless communication networks) in adistributed computing architecture.

The series characterization module 208 can perform the seriescharacterization process described above with reference to FIG. 1 . Tofacilitate this end, the series characterization module 208 can includeimage generation component 210, series characterization component 212,training component 214, characterization model database 216 andannotation component 218.

With reference to FIGS. 1 and 2 , the image generation component 210 cangenerate the representative image 106 (or images) for each medical imageseries included in the scan data 102. In this regard, in someimplementations, the scan data 102 may include only a single medicalimage series. With these implementations, the image generation component210 can generate one or more representative images for the singleseries. For example, as applied to CT scan data, if the scan mode iscine, then the representative image can represent the one cycle of theacquisition. In other implementations, the scan data 102 may include twoor more medical image series. With these implementations, the imagegeneration component 210 can identify the different series included inthe scan data 102 and generate a sperate representative image (orimages) for each of the different series. For example, if the scan data102 consists of multiple images at each image location obtained atdifferent scan cycles/times as in a perfusion scan, then the imagegeneration component 210 can generate one or more representative imagesfor each group of images within that scan where within each group thereis only image at each location. In another example, the scan data 102may include multiple series of different anatomical regions in the body,which is often the case the trauma workflow (e.g., sometimes up to adozen). In accordance with this example, the image generation component210 can identify the different series and generate a separaterepresentative image (or images) for each series.

For each series, the image generation component 210 may be configured togenerate a single representative image from a singleperspective/orientation of the 3D anatomy scan data. In otherimplementations, the image generation component 210 may be configured togenerate two or more representative images that provide different views,have different slice thicknesses, have different visual properties, andso on. The number and type of representative images generated for aseries can vary depending on the type of the series and other knowninformation about the series. For example, in some implementations, someof the series characterization models can be adapted to processdifferent representative input images that correspond to differentviews/perspectives of the 3D anatomy scan data. In otherimplementations, some of the series characterization models may beadapted to receive and process multiple input images (e.g., amulti-channel input model).

In some embodiments, the representative image or images can include oneor more of the scan images included in the medical image series. Withthese embodiments, the image generation component 210 does not performany image reconstruction/generation for obtaining the representativeimage(s). For example, the image generation component 210 can select thescan image included in the series that corresponds to the middle sliceas the representative image. In another example, the image generationcomponent 210 can select two or more scans at different scan locationsfor the representative images. For example, the image generationcomponent 210 can select the middle slice plus the first slice and thelast slice in the series as the representative images.

In other embodiments, the image generation component 210 can generateone or more new reconstructed images from the scan data 102 as therepresentative image or images using one or more 3D to 2D imagecompression techniques. The reconstructed image or images in theseembodiments are referred to as “new” because they are not originallyincluded in the scan data 102 and/or are not the type of 2D scan imagereconstructions that the imaging system 202 is configured to generate.Instead, the image generation component 210 can generate therepresentative image (or images) from the 2D and 3D image dataassociated with the series as provided in the scan data 102.

In various embodiments, similar to the scan images in the series, therepresentative image generated by the image generation component 210 caninclude or correspond to an MPR image. In some implementations, therepresentative image can be considered an MPR image that uses themaximum intensity projection (MIP) for the pixel values in the finalimage, referred to herein as an MPR-MIP image. In other implementations,the representative image can be considered an MPR image that uses theminimum intensity projection (mIP) for the pixel values in the finalimage, referred to herein as an MPR-mIP image (using a lowercase m todenote minimum and an uppercase M to denote maximum). The type of therepresentative image (or images) can also vary for different anatomiesand/or use-cases.

As noted above, the image generation process can be correlated to a 3Dto 2D image compression process, wherein the image generation component210 compresses a 3D volume image corresponding to a scanned anatomicalregion into a single 2D representation of that 3D volume image thatcaptures the most important features of the 3D volume image. In variousembodiments, this can involve selecting/defining a sub-volume of the 3Dvolume image for the compression and the orientation of thedimensionality of the compression (e.g., the specific axis ororientation for the 2D image relative to the 3D volume). For example,there are three standard anatomic planes that are generally used todisplay data for CT scans: axial (or horizontal), coronal (cutting thebody into slices that go from anterior to posterior (AP) or posterior toanterior (PA)), and sagittal or lateral (cutting the body into slicesthat go from right to left or left to right). However, 2D CT scan imagescan also be generated relative to other planes (e.g., oblique or evencurved planes). As described herein, reference to scan images beinggenerated relative to one axis (e.g., x, y or z) of a 3D coordinatesystem refers to the scan images being generated at different pointsalong the direction of the one axis such that each scan image isoriented relative to the same anatomical plane. The 3D volume image canbe included in the scan data 102 and/or generated by the imagegeneration component 210 by stacking the respective images included inthe series.

In some embodiments, the image generation component 210 can beconfigured to generate the representative image in a default orientation(e.g., axial). For example, the default orientation can be based on theorientation of the medical image series. In this regard, if the medicalimage series includes axial scan images, then the image generationcomponent 210 can select the axial orientation for the representativeimage. In other implementations, the image generation component 210 canbe configured to generate representative images in two or more defaultorientations (e.g., axial, coronal AP, coronal PA, and/orsagittal/lateral). The orientation of the representative image or imagescan vary depending on the type of scan and the use-case. The process forcompressing the 3D scan image data into the representative image canalso vary based on the selected orientation for the representativeimage. Techniques for selecting the sub-volume of the 3D image data forwhich to perform the compression can also vary. In some embodiments, theimage generation component 210 can be adapted to select a defaultsub-volume for any 3D volume image corresponding to the scanned regionrepresented in a medical image series. For instance, the defaultsub-volume can remove a defined portion of the 3D volume in one or moredirections in 3D from the boundary/periphery of the volume toward thecentroid of the volume. In other implementations, the sub-volumeselection can be based on the type of the scan, the specific anatomicalregion of interest scanned, the size/position of the subject, and otherfactors that may be known about the scan data 102.

Once the sub-volume and representative image orientation have beenselected, the 3D-to-2D compression process can vary. In someimplementations, the image generation component 210 can generate therepresentative image by selecting pixels from the 3D volume to beincluded in the MPR-MIP or MRP-mIP representative image based on thecentroid of the sub-volume (or original volume). The image generationcomponent 210 can also use a weighting function for the pixels, whereinpixels near the centroid of the selected sub-volume (or original volume)are given higher weights relative to peripheral pixels. The imagegeneration component 210 may also select the range of pixels in the 2Ddirections of the selected orientation for the representative image(e.g., x and y, x and z, z and y, etc.) based on the particularanatomical region scanned and/or the type of the scan. For example, asapplied to axial images, the image generation component 210 can selectthe range of pixels in the x and y direction based on the anatomycaptured. The image pixels can also be optionally thresholded to includeonly a particular range of Hounsfield units (HU)s of interest (e.g., toavoid metal objects or bone if that is desired). The MPR-MIP/mIPrepresentative images can also be generated with segmentation to removenon-patient related objects such as patient table or accessories. Theimage generation component 210 can also tailor the desired thickness forthe representative scan image based on the orientation, anatomy and/oruse-case.

FIG. 3 illustrates an example process 300 for generating one or morerepresentative images for a medical image scan series in accordance withone or more embodiments of the disclosed subject matter. Process 300illustrates three different types of example MRP-MIP representativeimages that can be generated (e.g., by the image generation component210) for a cardiac CT exam study. In accordance with process 300, theinput data 302 can include the CT exam series (which consists of aseries of 2D MRP images) and/or the corresponding 3D scan data 302 thatprovides a 3D representation of the anatomical region scanned. Usingthis input data, at 304 the image generation component 210 can generateone or more representative images for the scan series using thetechniques described above. In this example, the representative imagesinclude an MIP AP view 306, a thick axial MIP slab view 308 and an MIPlateral view 310.

The unique feature of these representations is the transformation of theseries of images into single image weighted by the centroid values ofpixels in the x and y directions so that the MPR-MIP image has the mostrepresentative view of the content of the human anatomy in the scannedseries. This is very important for extremity scanning when the scannedanatomy may not be centered perfectly in the gantry. Also, variousranges of pixels in the x and y directions from the axial images may beoptimally selected for different anatomies.

With reference again to FIG. 2 , the series characterization component212 can apply one or more series characterization models (or algorithms)to the representative image (or images) for each series to automaticallydetect and characterize defined characteristics of the series. Theseseries characterization models can be included in the characterizationmodel database 216 (or another suitable data structure accessible to theseries characterization module 208. These series characterization modelscan include a plurality of different classification type models thathave been trained to detect and characterize different characteristicsor features included (or excluded) in the representative image (orimages). The series characterization models can include various types ofmachine learning models that have been trained on training imagescorresponding to the representative image (or images) generated usingthe techniques described herein. The type or types of the machinelearning models can vary. In some embodiments, the seriescharacterization models can include deep learning models, suchconvolutional neural network (CNN)s, RESNET type models, and otherneural network models with different types of pooling techniques in theclassification block. Other suitable types of the machine learningmodels can include but are not limited to, generative adversarial neuralnetwork models (GANs), long short-term memory models (LSTMs),attention-based models, transformers, decision tree-based models,Bayesian network models, regression models and the like. These modelsmay be trained using supervised, unsupervised and/or semi-supervisedmachine learning techniques.

In some embodiments, the series characterization module 208 may includetraining component 214 to train and develop the series characterizationmodels based on training data provided by the medical image database 206that includes accurately annotated scan data (e.g., with thecharacteristics that the models are being trained to detect). Forexample, the training data can include a plurality of different types of3D imaging studies of the same modality (e.g., CT, MRI, PET, etc.)respectively including different types of medical image series, capturedwith different protocols and performed for a plurality of differentpatients. For each of the medical image studies in the training data,the image generation component 210 can employ the same representativeimage generation techniques to generate the representative images fortraining the respective series characterization models. The modeltraining process can generally be performed offline as a sperate phaseprior to the inferencing phase in which the series characterizationmodels are applied to the representative images in the field to generatethe scan series characteristics data 106. In some embodiments, thetraining component 214 can also regularly and/or continuously retrainand fine tune the series characterization models over time (afterdeployment) based on received user feedback regarding the accuracy ofthe model performance on new scan series data.

In one or more embodiments, the series characterization models caninclude a plurality of different models tailored to different scantypes, anatomical regions, and classification tasks. The seriescharacterization component 212 can select and apply the appropriatemodels for a particular set of scan data 102 and/or medical image seriesassociated therewith based on any known information about the series (asreceived as metadata with the scan data 102) and inferred informationlearned about the medical image series as a result of application of aone or more of the series characterization models. In this regard, theseries characterization component 212 can apply at least some of theseries characterization models in a sequential fashion, wherein theinference output of a preceding model influences the selection of thefollowing model. For example, based on a first model indicating a seriesdepicts a specific anatomical ROI, the series characterization component212 may select and apply a second series characterization model to therepresentative image (or images) based on the second model beingtailored to that specific anatomical ROI, wherein the second model isadapted to detect another characteristics about the series. The seriescharacterization component 212 can continue to select and applyadditional models to the representative image (or images) in thisfashion as applicable based on the outputs of the preceding models. Thenumber of series characterization models applied, and thecharacteristics inferred by each model can vary.

In various embodiments, the series characterization models can includeone or more primary models adapted to detect or classify the seriestype. With these embodiments, the series type can be one of a predefinedset of series type classifications. The number and description of theseries type classifications can vary depending on the scan modality(e.g., CT, MR, PET, etc.), and the system design. In one or moreembodiments as applied to CT scans, the series type can be one of about8-20 predefined type classifications that are based on the anatomicalregion or body part captured in the series.

FIGS. 4A-4C illustrate example MPR-MIP representative images generatedusing the techniques described above for different series typeclassifications. The series type classifications in this exampleinclude: abdomen, body, abdomen/pelvis, pelvis, CAP (chest, abdomen andpelvis), chest/abdomen, runoff, head, head/neck, C-spine, chest,extremity, TL-spine, L-spine, T-Spine, and facial bone. In this example,aside from the extremity category, the image generation component 210has generated two representative input images for each series category,one from the AP perspective and another from the lateral perspective.These example representative images can be used as input to the variousseries characterization models to detect the series type classificationalong with various other characteristics discussed below.

With reference again to FIG. 2 in view of FIG. 4 , in one or moreembodiments, the series characterization component 212 can be configuredto initially apply one or more series type classification models to therepresentative image (or images) generated for a series to determine theseries type classification. The series characterization model 212 canthen select and apply one or more secondary series characterizationmodels to the representative image (or images) based on the series typeclassification. With these embodiments, the secondary seriescharacterization models can include different subsets of models that aretailored to different series types. In some implementations of theseembodiments, the series characterization models can include a universaltype classification model that can receive any representative image(e.g., any of the representative images depicted in FIGS. 4A-4C forinstance), regardless of the anatomical region depicted, and generate anoutput classification that classifies the series type as one of thepossible type classification categories.

In other embodiments, the series characterization models can includeseparate models for each of the different type classifications and theseries characterization component 212 can be configured to apply each ofthe models to the representative image (or images) to determine whichone provides a positive classification. In some implementations of theseembodiments, in order to minimize errors attributed to false positivesand/or false negatives, the different series type classification modelscan be adapted to generate a binary output that classifies the inputimages as either having or not having the specific type classificationthat the model is adapted to detect and a confidence score indicative ofthe degree of confidence in the accuracy of the model's output. Forexample, assume there are 15 different series type classification modelseach adapted to classify the representative image (or images) as eitherhaving (e.g., a positive classification) or not having (e.g., a negativeclassification) the specific series type classification the model isadapted to detect and provide a confidence score reflective of thedegree of confidence in the result (e.g., as a percentage score with 0%being the lowest degree of confidence and 100% being the highest, oranother suitable scoring scale). The series characterization component212 can further evaluate the results of each of the models to select themost probable classification based on the binary classification and theconfidence score. For example, in some implementations, if only onemodel provides a positive result, the series characterization component212 can assume the series belongs to that series classification.However, if there is more than one positive result, the seriesclassification component 212 can select the classification with thehighest confidence score.

Additionally, or alternatively, the series classification component 212can select the top N positive results (e.g., top two or another number)with the top N highest confidence scores as possible typeclassifications for the series for further processing with additionalmore refined type classification models tailored to those categories tofurther drill down on the correct series type classification. In someimplementations of these embodiments, the image generation component 210can generate one or more additional or alternative representative imagesfor the series based on the potential type classification categories,wherein the additional representative images are tailored to thepotential type classification categories. For example, the imagegeneration component 210 can tailor the representative image generationparameters (e.g., the sub-volume dimensions, thedirectionality/orientation, the pixel weightings, the type of theprojection (MIP vs. mIP) and so on) based on the potential typeclassifications to generate one or more new representative images thatare tailored to the potential type classifications. For instance, if thepotential type classifications include chest and cardiac, the imagegeneration component 210 can generate one or more new representativeimages for the series that better represent the distinguishingcharacteristics between chest and cardiac. The series characterizationcomponent 212 can then reapply the corresponding series typeclassification models to the new representative images to drill down onthe correct classification. The image generation component 210 can alsogenerate one or more new representative images with different imagegeneration parameters for processing by the primary and/or secondaryseries characterization models based on measure of confidence in themodel outputs. For example, in some implementations, the seriescharacterization models can be adapted to generate a confidence scorethat reflects the level of confidence in the accuracy of the modeloutput. The image generation component 210 can further be adapted togenerate one or more new representative images with differentorientations/perspectives (or other image generation parameters) forprocessing by the models based on the confidence score being below athreshold level of confidence. For example, in the case of ambiguity ofa prediction from a first series characterization model with one type ofrepresentative image in a first orientation (e.g., anterior posterior(AP)), the image generation component 210 can generate anotherrepresentative image in a different orientation (e.g., lateral) forprocessing by the first series characterization model (or a differentseries characterization model) to provide an additional inference outputget down to a more definitive classification. The different outputs canfurther be aggregated to converge on a more accurate classification.

In some embodiments, the secondary series characterization models caninclude one or more contrast phase detection models adapted to detectwhether contrast injection was performed and if so, the particularcontrast phase reflected in the series. The contrast phases can beclassified as one of a defined set of possible contrast phases. Forexample, in some implementations, the defined set can include eithernon-contrast, arterial, portal/venous, and delayed. In otherimplementations, the defined set can include two or more of thefollowing phases: pre-contrast phase (or unenhanced phase), intravenous(IV) phase (IVP), arterial phase (AP), early AP, late AP, extracellularphase (ECP), portal venous phase (PVP), delayed phase (DP), transitionalphase (TP), hepatobiliary phase (HBP), and variants thereof. As notedabove, these contrast phase detection models can include differentmodels tailored to different series type classification. In this regard,the series characterization component 212 can select the appropriatecontrast phase detection model (or models) for applying to therepresentative image (or images) based the series type classification.For example, the contrast phase detection models can be optimized byanatomy. Accordingly, if a first series characterization model detects aparticular anatomical ROI and/or landmark present, the seriescharacterization component 212 can invoke the contrast phase detectionmodel for that anatomical ROI and/or landmark (e.g., cardiac, head,abdomen, etc.). The number of clinically relevant contrast phasespresent in a series often vary by anatomical region. Thus, by tailoringthe different contrast phase detection models to specific anatomies, therespective models will be adapted to detect the appropriate clinicallyrelevant contrast phases.

The secondary series characterization models can also include one ormore artifact detection models adapted to detect whether any artifactsare depicted in the representative image (or images) and thus theseries. These artifacts can include metal implants/devices as well asother foreign objects present in the body. In some implementations, theone or more artifact detection models can also be adapted to classifythe type of artifact/object detected and/or the relative anatomicallocation of the detected artifact. As noted above, in some embodiments,the one or more artifact detection models can include different modelstailored to different series type classifications and/or contrastphases. In this regard, the series characterization component 212 canselect the appropriate artifact detection model (or models) for applyingto the representative image (or images) based the series typeclassification and/or the contrast phase classification.

The secondary series characterization models can also include one ormore anatomy classification models adapted to classify specificanatomical features and/or ROIs depicted in the representative image (orimages) and thus the series. The anatomy classification models canprovide a more granular anatomy classification relative to the seriesclassification models. Similar to the other secondary classificationmodels, in some embodiments, the one or more anatomy detection modelscan include different models tailored to different series typeclassifications and/or contrast phases. In this regard, the seriescharacterization component 212 can select the appropriate anatomyclassification model (or models) for applying to the representativeimage (or images) based the series type classification and/or thecontrast phase classification.

The secondary series classification models can also include additionalcharacterization models adapted to detect additional anatomy specificand/or contrast phase specific characteristics of a medical image seriesbased on the representative image (or images). For example, someadditional characteristics can include, but are not limited to, imagequality characteristics such as banding artifacts, rings, streaks, etc.,and relative position and alignment of specific anatomical landmarksthat are relevant to a particular type of scan (e.g., alignment positionof head for a brain scan).

In some embodiments, the image generation component 210 can alsogenerate one or more additional or alternative representative images forthe series based on its type classification, contrast phaseclassification, and/or other inferred characteristics for input to anadditional secondary series characterization model, wherein theadditional representative images or images are tailored to the otheradditional series characterization model. For instance, the imagegeneration component 210 may generate a new representative image for aseries based on its series type classification for input to a contrastphase detection model and/or artifact detection model. With theseembodiments, the image generation component 210 can tailor the newrepresentative image generation parameters (e.g., the sub-volumedimensions, the directionality/orientation, the pixel weightings, thetype of the projection (MIP vs. mIP), and so on) based on its typeclassification to generate one or more new representative images thatare tailored to the type classifications.

The result of the series characterization process can include one ormore automatically detected characteristics of the respective seriesincluded in the scan data 102. The annotation component 218 can furthergenerate annotation metadata (e.g., scan series characteristics data106) describing the one or more characteristics and associate theannotation metadata with the respective series as stored in one or moredatabases (e.g., the medical image database 206 or another suitabledatabase). The annotation component 218 can further structure theannotation metadata according to a defined protocol or format, such asthe DICOM standard or a similar standard.

The scan series characteristics data 106 can further be employed toautomatically invoke applicable post-processing tasks, image analysistask, and reporting tasks as well as optimize the visualizationworkflows.

In this regard, the system 200 can include visualization component 222to facilitate reviewing the medical image series via a medical imagingapplication accessed via the user device. For example, the medicalimaging application can include a web-application, a native application,a hybrid application or the like, that provides for accessing medicalimage series provided in the medical image database 206,reviewing/viewing the medial image series, generating radiologistreports, annotating the medical images, and/or executing variouspost-processing tasks on the images. In some implementations, thevisualization component 222 can include or otherwise provide the medicalimaging application. In this regard, many existing medical imagingapplications require the user (e.g., the radiologist, the technician,etc.) to manually select and set up the visualization layout as desiredbased on the series type, the contrast phase present and othercharacteristics about the series. With the disclosed techniques, thevisualization component 222 can automatically generate the visualizationlayout as tailored to the medical image series based on theautomatically detected scan characteristics. For example, thevisualization component 222 can determine the number of windows, thesize of the respective windows, the different images or groups of imagesto include in the respective windows, the different imageviews/perspectives to include in the respective windows, the annotationtools to provide/activate, the editing tools to provide/activate, and soon, based on the series type classification, the contrast phase orphases detected, and other automatically detected scan characteristics.The visualization component 222 can also include (or the user device caninclude) a rendering component that renders the medical image seriesaccording to the visualization layout in association with reviewing themedical image series via the medical imaging application.

For example, FIG. 5 present an example visualization layout 500 for abrain scan series that may be generated by the visualization component222 based on automatically detected scan characteristics of the brainscan series. FIG. 6 present another example visualization layout 600 fora trauma torso series that may be generated by the visualizationcomponent 222 based on automatically detected scan characteristics ofthe trauma torso series. As can be seen by comparison of visualizationlayout 500 and 600, the design of the layout can vary significantly fordifferent types of series.

With reference again to FIG. 2 , in addition to automatically tailoringthe visualization layout and tools, the system 200 can also include afeedback component 224 that can provide the scan series characteristicsdata 106 as feedback data to an operating technician of the medicalimaging system 202 (e.g., the canner device) to facilitate reviewingwhether the medical image series reflects one or more targetcharacteristics for the performance of the scan while the patient ispositioned relative to the medical image scanner device. For example,the reception component 220 can receive the scan data 102 for processingby the series characterization module 208 while the patient is stilllaying on the scanner table. The inferred scan series characteristicsdata 106 can further be provided to the operating technician via acomputing device employed by the technician (e.g., user device 230)prior to completion of the scanning session. The technician can thenreview the results and determine whether to rescan the patient as neededto obtain the desired scan characteristics. Additionally, oralternatively, the scan series characteristics data 106 can be presentedto the technician in association with an automated annotation softwaretool that facilitates more efficiently entering annotation tags for ascan series by the technician in accordance with a standardized protocol(e.g., DICOM or the like). For example, as opposed to manually enteringindividual scan characteristic DICOM tags for a medical image series,the software can allow the technician to review the automaticallydetected characteristics and either accept and apply the annotation tothe medical image series, edit the detected characteristics or rejectthe characteristics.

The similar case study component 226 can further provide forautomatically finding and pulling similar cases for a current medicalimage series based on the automatically detected scan characteristics.In particular, the similar case study component 226 can accesses amedical image database comprising a plurality of different medical imagestudies (e.g., medical image database 206), identify a subset (e.g., oneor more) of the different medical images studies that are similar to themedical image series based on the one or more characteristics, andextract the subset for performance of comparative analysis between thesubset and the medical image series. For example, the similar cases canbe presented to a radiologist (or another reviewing entity) via thevisualization application in association with reviewing the currentmedical image series. In another example, the similar cases can beaggregated and used for additional post-processing tasks andapplications (e.g., model training, receiving expert review, used forcontinued learning and reporting, etc.). In some implementation, thesimilar case study component 226 can also be configured to find and pullsimilar medical image exams (e.g., series) for the same patient for thepurpose of performing longitudinal studies. In this regard, the similarcase study component 226 can employ an identifier for the patientassociated with the current medical image series and find and pull(e.g., extract) other cases for the same patient identifier that wereperformed in the past and have one or more of the same or similar seriescharacteristics (e.g., same series type, same contrast phase, etc.).

The post-processing component 228 can also select and apply one or morepost-processing tasks for processing the medical image series based onthe one or more automatically detected scan series characteristics. Thetypes of the post-processing tasks can vary. For example, thepost-processing tasks can include automatic application of one or moreimage processing models/algorithms adapted to process medical images inthe series. These image processing algorithms can include variousmedical image inferencing algorithms or models (e.g., AI models). Forexample, the image processing algorithms can include, but are notlimited to, image restoration algorithms (e.g., used to improve thequality of the image), image analysis algorithms (e.g.,classification/diagnosis models, organ segmentation models, etc.), imagesynthesis algorithms (e.g., used construct a three-dimensional imagebased on multiple two-dimensional images images), image enhancementalgorithms (e.g., used improve the image by using filters or addinginformation that will assist with visualization), and image compressionalgorithms (e.g., used to reduce the size of the image to enhancetransmission times and storage required). The post-processing component228 can automatically invoke and apply the appropriate image processingmodels for a series based on the specific scan characteristicsautomatically detected by the series characterization models. Forexample, in an implementation in which a series characterization modelincludes an artifact detection model that detected presence of anartifact in the one or more representative images, the post-processingcomponent 228 can automatically select and apply an artifactreduction/correction image processing model on one or more of theoriginal images in the series to remove/correct artifacts includedtherein. Further, the artifact reduction/correction image processingmodels can be tailored to specific types of anatomies and/or specifictypes of artifacts. In this regard, the post-processing component 228can select and apply the appropriate artifact reduction/correction imageprocessing model based on the detected series type, anatomy present, andthe specific type of artifact detected.

The image processing algorithms may be stored at one or more networkaccessible locations (not shown) that may be accessed and applied by thepost-processing component 228. In various embodiments, the imageprocessing algorithms can be integrated into the predefined workflows asHTTP tasks and accessed and applied to the medical images asweb-services in accordance with the pre-defined workflows. Thepre-defined workflows can be tailored to specific medical image seriestypes and other characteristics of the medical image series that arecapable of being automatically detected by the series characterizationmodule 208. Additionally, or alternatively, the algorithms/models can beintegrated into workflows as “jobs.” Specifically, an algorithm/modeland other tasks defined by computer executable instructions can bewrapped in a Kubernetes Job function and the algorithm orchestrationcomponent can execute it asynchronously inside the cluster on behalf ofthe user. This feature opens multiple possibilities specially related tolegacy systems where the client service is not under HTTP and is only asimple command line and/or executable.

FIG. 7 illustrates a flow diagram of an example process 700 forautomatically detecting scan characteristics of a medical image seriesin accordance with one or more embodiments of the disclosed subjectmatter. Process 700 provides an example process that can be performed bysystem 200. In accordance with process 700 at 702, the system cangenerate one or more representative images for each scan series includedin the scan data 102. For each scan series, at 704, the system can applyan initial characterization model to the representative image todetermine a first series characteristic (e.g., series type). At 706, thesystem can select and apply an appropriate secondary characterizationmodel to the representative image based on the first seriescharacteristic to determine a secondary series characteristic (e.g.,contrast phase). At 706, the system can determine whether any additionalseries characteristic models are available that are applicable to thescan series based on the output of the preceding models. If not, thenprocess 700 can aggregate the scan series characteristics at 712. Ifadditional models are applicable, then at 710, the system can select andapply the applicable additional characterization model(s) to therepresentative image(s) based on the output characteristics of thepreceding characterization models to determine additionalcharacteristics. The system can repeat this process until all applicablemodels have been applied in a daisy-chain fashion. Once all applicablemodels have been applied and the characteristics have been aggregated,at 714, the system can automatically select and perform one or moretasks based on the aggregated scan series characteristics.

FIG. 8 illustrates a flow diagram of another example process 800 forautomatically detecting scan characteristics of a medical image seriesin accordance with one or more embodiments of the disclosed subjectmatter. Process 800 provides another example process that can beperformed by system 200. In accordance with process 800 at 802, thesystem can generate a first representative image for each scan seriesincluded in the scan data 102. For example, the first representativeimage may include a default MPR-MIP reconstruction using reconstructionparameters that are not anatomy specific. For each scan series, at 804,the system can apply the one or more series type characterization modelsto the first representative image to determine the series type. At 808,the system can determine whether any additional representative imagesare needed for the series type. For example, the series characterizationcomponent 212 can determine based on the series type, what types andcharacteristics of representative images are needed as input tosubsequent available series characterization models for that series typeand direct the image generation component 210 to generate new oradditional representative images with reconstruction parameters that aretailored to that series type (e.g., anatomy specific). In this regard,at 806, if no additional representative images are needed, then process800 can continue to 810. However, if additional representative imagesare needed for the particular series type, then at 808, the system cangenerate the additional representative image (or images) for the seriestype.

At 810, the system can select and apply an appropriate secondarycharacterization model to the representative image based on the seriestype to determine a secondary series characteristic (e.g., contrastphase). At 812, the system can determine whether any additional seriescharacteristic models are available that are applicable to the scanseries based on the output of the preceding models. If not, then process800 can aggregate the scan series characteristics at 816. If additionalmodels are applicable, then at 814, the system can select and apply theapplicable additional characterization model(s) to the representativeimage(s) based on the output characteristics of the precedingcharacterization models to determine additional characteristics. Thesystem can repeat this process until all applicable models have beenapplied in a daisy-chain fashion. Once all applicable models have beenapplied and the characteristics have been aggregated, at 818, the systemcan automatically select and perform one or more tasks based on theaggregated scan series characteristics.

FIG. 9 presents a high-level flow diagram of an examplecomputer-implemented process 900 for automatically detecting scancharacteristics of a medical image series in accordance with one or moreembodiments of the disclosed subject matter. Repetitive description oflike elements employed in respective embodiments is omitted for sake ofbrevity.

In accordance with process 900, at 902 a system operatively coupled to aprocessor (e.g., system 100 or the like), generates a representativeimage of a medical image series comprising a plurality of scan images(e.g., using image generation component 210). At 904, the system appliesone or more characteristic detection algorithms to the representativeimage to determine one or more characteristics of the medical imageseries (e.g., using series characterization component 212). At 906, thesystem performs one or more processing tasks related to the medicalimage series selected based on the one or more scan characteristics(e.g., using visualization component 222, feedback component 224,similar case study component 226, and/or post-processing component 228).

EXAMPLE OPERATING ENVIRONMENT

One or more embodiments can be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product can include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium can be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection can be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It can be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions can be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionscan also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

In connection with FIG. 10 , the systems and processes described belowcan be embodied within hardware, such as a single integrated circuit(IC) chip, multiple ICs, an application specific integrated circuit(ASIC), or the like. Further, the order in which some or all of theprocess blocks appear in each process should not be deemed limiting.Rather, it should be understood that some of the process blocks can beexecuted in a variety of orders, not all of which can be explicitlyillustrated herein.

With reference to FIG. 10 , an example environment 1000 for implementingvarious aspects of the claimed subject matter includes a computer 1002.The computer 1002 includes a processing unit 1004, a system memory 1006,a codec 1035, and a system bus 1008. The system bus 1008 couples systemcomponents including, but not limited to, the system memory 1006 to theprocessing unit 1004. The processing unit 1004 can be any of variousavailable processors. Dual microprocessors and other multiprocessorarchitectures also can be employed as the processing unit 1004.

The system bus 1008 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 1094), and SmallComputer Systems Interface (SCSI).

The system memory 1006 includes volatile memory 1010 and non-volatilememory 1012, which can employ one or more of the disclosed memoryarchitectures, in various embodiments. The basic input/output system(BIOS), containing the basic routines to transfer information betweenelements within the computer 1002, such as during start-up, is stored innon-volatile memory 1012. In addition, according to present innovations,codec 1035 can include at least one of an encoder or decoder, whereinthe at least one of an encoder or decoder can consist of hardware,software, or a combination of hardware and software. Although, codec1035 is depicted as a separate component, codec 1035 can be containedwithin non-volatile memory 1012. By way of illustration, and notlimitation, non-volatile memory 1012 can include read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), Flash memory, 3D Flashmemory, or resistive memory such as resistive random access memory(RRAM). Non-volatile memory 1012 can employ one or more of the disclosedmemory devices, in at least some embodiments. Moreover, non-volatilememory 1012 can be computer memory (e.g., physically integrated withcomputer 1002 or a mainboard thereof), or removable memory. Examples ofsuitable removable memory with which disclosed embodiments can beimplemented can include a secure digital (SD) card, a compact Flash (CF)card, a universal serial bus (USB) memory stick, or the like. Volatilememory 1010 includes random access memory (RAM), which acts as externalcache memory, and can also employ one or more disclosed memory devicesin various embodiments. By way of illustration and not limitation, RAMis available in many forms such as static RAM (SRAM), dynamic RAM(DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM),and enhanced SDRAM (ESDRAM) and so forth.

Computer 1002 can also include removable/non-removable,volatile/non-volatile computer storage medium. FIG. 10 illustrates, forexample, disk storage 1014. Disk storage 1014 includes, but is notlimited to, devices like a magnetic disk drive, solid state disk (SSD),flash memory card, or memory stick. In addition, disk storage 1014 caninclude storage medium separately or in combination with other storagemedium including, but not limited to, an optical disk drive such as acompact disk ROM device (CD-ROM), CD recordable drive (CD-R Drive), CDrewritable drive (CD-RW Drive) or a digital versatile disk ROM drive(DVD-ROM). To facilitate connection of the disk storage 1014 to thesystem bus 1008, a removable or non-removable interface is typicallyused, such as interface 1016. It is appreciated that disk storage 1014can store information related to a user. Such information might bestored at or provided to a server or to an application running on a userdevice. In one embodiment, the user can be notified (e.g., by way ofoutput device(s) 1036) of the types of information that are stored todisk storage 1014 or transmitted to the server or application. The usercan be provided the opportunity to opt-in or opt-out of having suchinformation collected or shared with the server or application (e.g., byway of input from input device(s) 1028).

It is to be appreciated that FIG. 10 describes software that acts as anintermediary between users and the basic computer resources described inthe suitable operating environment 1000. Such software includes anoperating system 1018. Operating system 1018, which can be stored ondisk storage 1014, acts to control and allocate resources of thecomputer 1002. Applications 1020 take advantage of the management ofresources by operating system 1018 through program modules 1024, andprogram data 1026, such as the boot/shutdown transaction table and thelike, stored either in system memory 1006 or on disk storage 1014. It isto be appreciated that the claimed subject matter can be implementedwith various operating systems or combinations of operating systems.

A user enters commands or information into the computer 1002 throughinput device(s) 1028. Input devices 1028 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1004through the system bus 1008 via interface port(s) 1030. Interfaceport(s) 1030 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1036 usesome of the same type of ports as input device(s) 1028. Thus, forexample, a USB port can be used to provide input to computer 1002 and tooutput information from computer 1002 to an output device 1036. Outputadapter 1034 is provided to illustrate that there are some outputdevices 1036 like monitors, speakers, and printers, among other outputdevices 1036, which require special adapters. The output adapters 1034include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1036and the system bus 1008. It should be noted that other devices orsystems of devices provide both input and output capabilities such asremote computer(s) 1038.

Computer 1002 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1038. The remote computer(s) 1038 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device, a smart phone, a tablet, or other network node, andtypically includes many of the elements described relative to computer1002. For purposes of brevity, only a memory storage device 1040 isillustrated with remote computer(s) 1038. Remote computer(s) 1038 islogically connected to computer 1002 through a network interface 1042and then connected via communication connection(s) 1044. Networkinterface 1042 encompasses wire or wireless communication networks suchas local-area networks (LAN) and wide-area networks (WAN) and cellularnetworks. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL).

Communication connection(s) 1044 refers to the hardware/softwareemployed to connect the network interface 1042 to the bus 1008. Whilecommunication connection 1044 is shown for illustrative clarity insidecomputer 1002, it can also be external to computer 1002. Thehardware/software necessary for connection to the network interface 1042includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and wired and wirelessEthernet cards, hubs, and routers.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration and are intended to be non-limiting. For the avoidanceof doubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as an“example” and/or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM). Additionally, the disclosed memorycomponents of systems or computer-implemented methods herein areintended to include, without being limited to including, these and anyother suitable types of memory.

What has been described above include mere examples of systems andcomputer-implemented methods. It is, of course, not possible to describeevery conceivable combination of components or computer-implementedmethods for purposes of describing this disclosure, but one of ordinaryskill in the art can recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim. The descriptions of the various embodiments have been presentedfor purposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationscan be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A system, comprising: a memory that storescomputer executable components; and a processor that executes thecomputer executable components stored in the memory, wherein thecomputer executable components comprise: an image generation componentthat generates a representative image of a medical image seriescomprising a plurality of scan images; and a series characterizationcomponent that processes the representative image using one or morecharacteristic detection algorithms to determine one or morecharacteristics of the medical image series.
 2. The system of claim 1,wherein the computer executable components further comprise: anannotation component that generates annotation metadata describing theone or more characteristics and associates the annotation metadata withthe image series as stored in one or more databases.
 3. The system ofclaim 1, wherein the computer executable components further comprise: avisualization component that generates a visualization layout forreviewing the medical image series via a medical imaging applicationbased on the one or more characteristics; and a rendering component thatrenders the medical image series according to the visualization layoutin association with reviewing the medical image series via the medicalimaging application.
 4. The system of claim 1, wherein the computerexecutable components further comprise: a post-processing component thatselects and applies one or more post-processing tasks for processing themedical image series based on the one or more characteristics.
 5. Thesystem of claim 4, wherein the one or more post-processing taskscomprise one or more clinical inferencing tasks performed using one ormore clinical inferencing models.
 6. The system of claim 1, wherein thecomputer executable components further comprise: a similar case studycomponent that accesses a medical image database comprising a pluralityof different medical image studies, identifies a subset of the differentmedical images studies that are similar to the medical image seriesbased on the one or more characteristics, and extracts the subset forperformance of comparative analysis between the subset and the medicalimage series.
 7. The system of claim 6, wherein the medical imagesseries represents a patient identity and wherein the similar case studyfurther identifies the subset based on the subset comprising one or moremedical image studies performed for the patient identity in associationwith the comparative analysis being a longitudinal analysis for thepatient identity.
 8. The system of claim 1, wherein the computerexecutable components further comprise: a reception component thatreceives the medical image series in association with performance of athree-dimensional imaging scan of a patient while the patient ispositioned relative to a medical image scanner device; and a feedbackcomponent that provides feedback data describing the one or morecharacteristics to an operating technician of the medical image scannerdevice to facilitate reviewing whether the medical image series reflectsone or more target characteristics for the performance of thethree-dimensional imaging scan while the patient is positioned relativeto the medical image scanner device.
 9. The system of claim 1, whereinthe one or more characteristic detection algorithms comprise a series ofdetection algorithms applied to the representative image in series andwherein the scan characterization component selects subsequent detectionalgorithms in the series based on results of preceding detectionalgorithms in the series.
 10. The system of claim 1, wherein the one ormore characteristics detection algorithms comprise a first detectionalgorithm and a second detection algorithm, and wherein the scancharacterization component selects the second detection algorithm fromamongst a plurality of second detection algorithms based on the resultsof the first detection algorithm.
 11. The system of claim 10, whereinthe first detection algorithm comprises a scan type detection algorithmadapted to detect a type of the medical image series wherein the seconddetection algorithms are tailored to different types of medical imageseries.
 12. The system of claim 1, wherein the one or morecharacteristic detection algorithms are selected from the groupconsisting of: a scan type detection algorithm, a contrast phasedetection algorithm, an anatomic region detection algorithm, and foreignobject detection algorithm.
 13. The system of claim 1, wherein medicalimage series is associated with three-dimensional image datarepresentative of a three-dimensional volume of an anatomical region ofa patient, and wherein the image generation component generates therepresentative image from the three-dimensional image data using athree-dimensional to two-dimensional (3D to 2D) image compressionprocesses.
 14. A method, comprising: generating, by a system operativelycoupled to a processor, a representative image of a medical image seriescomprising a plurality of scan images; and applying, by the system, oneor more characteristic detection algorithms to the representative imageto determine one or more characteristics of the medical image series.15. The method of claim 14, further comprising: generating, by thesystem, annotation metadata describing the one or more characteristics;and associating, by the system, the annotation metadata with the imageseries as stored in one or more databases.
 16. The method of claim 14,further comprising: generating, by the system, a visualization layoutfor reviewing the medical image series via a medical imaging applicationbased on the one or more characteristics; and rendering, by the system,the medical image series according to the visualization layout inassociation with reviewing the medical image series via the medicalimaging application.
 17. The method of claim 14, further comprising:selecting, by the system, one or more post-processing tasks forprocessing the medical image series based on the one or morecharacteristics; and executing, by the system, the one or more post-postprocessing tasks based on the selecting.
 18. The method of claim 14,further comprising: accessing, by the system, a medical image databasecomprising a plurality of different medical image studies; identifying,by the system, a subset of the different medical images studies that aresimilar to the medical image series based on the one or morecharacteristics; and extracting, by the system, the subset forperformance of comparative analysis between the subset and the medicalimage series.
 19. The method of claim 14, wherein the one or morecharacteristic detection algorithms comprise a series of detectionalgorithms applied to the representative image in series and wherein themethod further comprises selecting subsequent detection algorithms inthe series based on results of preceding detection algorithms in theseries.
 20. The method of claim 14, wherein the one or morecharacteristic detection algorithms are selected from the groupconsisting of: a scan type detection algorithm, a contrast phasedetection algorithm, an anatomic region detection algorithm, and foreignobject detection algorithm.
 21. The method of claim 14, wherein medicalimage series is associated with three-dimensional image datarepresentative of a three-dimensional volume of an anatomical region ofa patient, and wherein the generating, comprises generating therepresentative image from the three-dimensional image data using athree-dimensional to two-dimensional (3D to 2D) image compressionprocesses.
 22. A machine-readable storage medium, comprising executableinstructions that, when executed by a processor, facilitate performanceof operations, comprising: generating a representative image of amedical image series comprising a plurality of scan images; applying oneor more characteristic detection algorithms to the representative imageto determine one or more characteristics of the medical image series;and performing one or more processing tasks related to the medical imageseries selected based on the one or more scan characteristics.
 23. Themachine-readable storage medium of claim 22, wherein the one or morecharacteristic detection algorithms comprise a series of detectionalgorithms applied to the representative image in series and wherein theoperations further comprise selecting subsequent detection algorithms inthe series based on results of preceding detection algorithms in theseries.